Are your Valid to timeframes decreasing due to AI?

[Pam Calvey]

We will soon be implementing an AI Agent to search our knowledge bases and are taking a stronger stance with our knowledge managers about timely workflow maintenance to ensure our articles are current and correct. Currently we have about 4,600 articles and a 365-day review time frame. I've heard of larger companies/knowledge bases having much shorter review cycles. What are others doing, and if you use an AI agent, do you see benefit to a quicker review cycle? 

[owenbarber]

Hi Pam, I think i see it that review cycles will naturally be shorter due to the information and feedback from the AI interactions which will almost negate Valid to apart from those polices etc that need that review cadence. 

I say this because as the information comes back from AI interactions the authors\owners will need to act quick to update that content to fill the gaps and tweak the content, much sooner than the 365 mandated check. 

[MaryG]

Great timing on this question — we're seeing the same conversation across enterprise KM implementations right now.

The short answer: yes, AI adoption should prompt a review of your Valid To cadence, but the more important shift is moving from time-based review to signal-based review.

A flat 365-day cycle treats a policy article the same as a how-to troubleshooting guide. When an AI Agent is surfacing that content, the stakes are higher — a stale article doesn't just frustrate a user, it generates a bad AI response. We recommend segmenting your review cycle by article type and volatility:

• Policy/compliance articles: Keep structured review cadence (90–180 days), tied to change management
• How-to/procedural articles: Shift to signal-based triggers — flagged AI interactions, low feedback scores, Article Quality Index alerts, or post-incident review
• FAQ/reference articles: Review on consumption signals — declining views, low helpfulness ratings, or Now Assist hallucination flags

The AI interaction feedback loop is your best governance tool here. Now Assist in AI Search surfaces Q&A gap data — articles that were retrieved but didn't satisfy the query. Build a workflow that routes those signals back to knowledge owners as review triggers, not just wait for the calendar.

For 4,600 articles, I'd also recommend running an Article Quality Index (AQI) baseline scan before your AI Agent goes live. It'll surface the content most likely to return poor AI responses so you can triage proactively rather than reactively.

Hope this helps!!